IMPROVING DEEP-LEARNING-BASED FACE RECOGNITION TO INCREASE ROBUSTNESS AGAINST MORPHING ATTACKS
Una M. Kelly, Luuk Spreeuwers and Raymond Veldhuis University of Twente, The Netherlands
State-of-the-art face recognition systems (FRS) are vulnerable to morphing attacks, in which two photos of different people are merged in such a way that the resulting photo resembles both people. Such a photo could be used to apply for a passport, allowing both people to travel with the same identity document. Research has so far focussed on developing morphing detection methods. We suggest that it might instead be worthwhile to make face recognition systems themselves more robust to morphing attacks. We show that deep-learning-based face recognition can be improved simply by treating morphed images just like real images during training but also that, for significant improvements, more work is needed. Furthermore, we test the performance of our FRS on morphs of a type not seen during training. This addresses the problem of overfitting to the type of morphs used during training, which is often overlooked in current research.
Biometrics, Morphing Attack Detection, Face Recognition, Vulnerability of Biometric Systems .
Full Paper
https://aircconline.com/csit/papers/vol10/csit101901.pdf
Volume Link :
http://airccse.org/csit/V10N19.html
ON SOME DESIRED PROPERTIES OF DATA AUGMENTATION BY ILLUMINATION SIMULATION FOR COLOR CONSTANCY
Nikola Banić1, Karlo Koščević2 , Marko Subašić2 , and Sven Lončarić2 1Gideon Brothers, Croatia 2University of Zagreb, Croatia
Computational color constancy is used in almost all digital cameras to reduce the influence of scene illumination on object colors. Many of the highly accurate published illumination estimation methods use deep learning, which relies on large amounts of images with known ground-truth illuminations. Since the size of the appropriate publicly available training datasets is relatively small, data augmentation is often used also by simulating the appearance of a given image under another illumination. Still, there are practically no reports on any desired properties of such simulated images or on the limits of their usability. In this paper, several experiments for determining some of these properties are proposed and conducted by comparing the behavior of the simplest illumination estimation methods on images of the same scenes obtained under real illuminations and images obtained through data augmentation. The experimental results are presented and discussed.
Color constancy, data augmentation, illumination estimation, image enhancement, white Balancing.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101903.pdf
Volume Link :
http://airccse.org/csit/V10N19.html
RESEARCH ON NOISE REDUCTION AND ENHANCEMENT OF WELD IMAGE
Xiang-Song Zhang1 , Wei-Xin Gao1 and Shi-Ling Zhu2 1,2Xi'an Shiyou University,China
In order to eliminate the salt pepper and Gaussian mixed noise in X-ray weld image, the extreme value characteristics of salt and pepper noise are used to separate the mixed noise, and the non local mean filtering algorithm is used to denoise it. Because the smoothness of the exponential weighted kernel function is too large, it is easy to cause the image details fuzzy, so the cosine coefficient based on the function is adopted. An improved non local mean imagedenoising algorithm is designed by using weighted Gaussian kernel function. The experimental results show that the new algorithm reduces the noise and retains the details of the original image, and the peak signal-to-noise ratio is increased by 1.5 dB. An adaptive salt and pepper noise elimination algorithm is proposed, which can automatically adjust the filtering window to identify the noise probability. Firstly, the median filter is applied to the image, and the filtering results are compared with the pre filtering results to get the noise points. Then the weighted average of the middle three groups of data under each filtering window is used to estimate the image noise probability. Before filtering, the obvious noise points are removed by threshold method, and then the central pixel is estimated by the reciprocal square of the distance from the center pixel of the window. Finally, according to Takagi Sugeno (T-S) fuzzy rules, the output estimates of different models are fused by using noise probability. Experimental results show that the algorithm has the ability of automatic noise estimation and adaptive window adjustment. After filtering, the standard mean square deviation can be reduced by more than 20%, and the speed can be increased more than twice. In the enhancement part, a nonlinear image enhancement method is proposed, which can adjust the parameters adaptively and enhance the weld area automatically instead of the background area. The enhancement effect achieves the best personal visual effect. Compared with the traditional method, the enhancement effect is better and more in line with the needs of industrial field.
X-ray image, Mixed noise, Noise separation, noise reduction, image enhancement.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101902.pdf
Volume Link :
http://airccse.org/csit/V10N19.html
IMAGE WAFER INSPECTION BASED ON TEMPLATE MATCHING
Massimiliano Barone STMicroelectronics, Agrate Brianza, Milano, Italy
This paper presents a template matching technique for detecting defects in VLSI wafer images. This method is based on traditional techniques of image analysis and image registration, but it combines the prior art of image wafer inspection in a new way, using prior knowledge like the design layout of VLSI wafer manufacturing process. This technique requires a golden template of the patterned wafer image under inspection which is obtained from the wafer image itself mixed to the layout design schemes. First a mapping between physical space and pixel space is needed. Then a template matching is applied for a more accurate alignment between wafer device and template. Finally, a segmented comparison is used for finding out possible defects. Results of the proposed method are presented in terms of visual quality of defect detection, any misalignment at topology level and number of correctly detected defective devices.
Wafer inspection, template matching, image registration, pattern recognition, VLSI wafer images, Golden template, segmented comparison, space mapping.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101505.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
AN EFFICIENT LANGUAGE-INDEPENDENT MULTI-FONT OCR FOR ARABIC SCRIPT
Hussein Osman, Karim Zaghw, Mostafa Hazem and Seifeldin Elsehely, Cairo University, Egypt
Optical Character Recognition (OCR) is the process of extracting digitized text from images of scanned documents. While OCR systems have already matured in many languages, they still have shortcomings in cursive languages with overlapping letters such as the Arabic language. This paper proposes a complete Arabic OCR system that takes a scanned image of Arabic Naskh script as an input and generates a corresponding digital document. Our Arabic OCR system consists of the following modules: Pre-processing, Word-level Feature Extraction, Character Segmentation, Character Recognition, and Post-processing. This paper also proposes an improved font-independent character segmentation algorithm that outperforms the state-of-the-art segmentation algorithms. Lastly, the paper proposes a neural network model for the character recognition task. The system has experimented on several open Arabic corpora datasets with an average character segmentation accuracy 98.06%, character recognition accuracy 99.89%, and overall system accuracy 97.94% achieving outstanding results compared to the state-of-the-art Arabic OCR systems.
Arabic OCR, Word Segmentation, Character Segmentation, Character Recognition, Neural Network.
For More Details :
: https://aircconline.com/csit/papers/vol10/csit101506.pdf
Volume Link :
http://airccse.org/csit/V10N15.html
PHONE CLUSTERING METHODS FOR MULTILINGUAL LANGUAGE IDENTIFICATION
Ronny Mabokela University of Johannesburg, Johannesburg, South Africa
This paper proposes phoneme clustering methods for multilingual language identification (LID) on a mixed-language corpus. A one-pass multilingual automated speech recognition (ASR) system converts spoken utterances into occurrences of phone sequences. Hidden Markov models were employed to train multilingual acoustic models that handle multiple languages within an utterance. Two phoneme clustering methods were explored to derive the most appropriate phoneme similarities between the target languages. Ultimately a supervised machine learning technique was employed to learn the language transition of the phonotactic information and engage the support vector machine (SVM) models to classify phoneme occurrences. The system performance was evaluated on mixed-language speech corpus for two South African languages (Sepedi and English) using the phone error rate (PER) and LID classification accuracy separately. We show that multilingual ASR which fed directly to the LID system has a direct impact on LID accuracy. Our proposed system has achieved an acceptable phone recognition and classification accuracy in mixed-language speech and monolingual speech (i.e. either Sepedi or English). Data-driven, and knowledge-driven phoneme clustering methods improve ASR and LID for code-switched speech. The data-driven method obtained the PER of 5.1% and LID classification accuracy of 94.5% when the acoustic models are trained with 64 Gaussian mixtures per state.
Code-switching, Phone clustering, Multilingual speech recognition, Mixed-language, Language Identification
For More Details :
https://aircconline.com/csit/papers/vol10/csit101421.pdf
Volume Link :
http://airccse.org/csit/V10N14.html
Journals by Area
Conference Publicity
To list your conference in this page, please contact us
